Road Grade Prediction for Predictive Energy Management in Hybrid Electric Vehicles

Hongwen He; Jinquan Guo; Chao Sun

doi:10.1016/j.egypro.2017.03.700

Road Grade Prediction for Predictive Energy Management in Hybrid Electric Vehicles

Hongwen He, Jinquan Guo, Chao Sun^*

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

16 Citations (Scopus)

Abstract

The uncertainty caused by the varying of road grades plays a critical role in impacting the hybrid electric vehicles (HEV) energy management performance, and therefore the fuel economy. This paper presents an autoregressive integrated moving average (ARIMA) based method, aiming to forecast the near future road grade in real-time with acceptable accuracy for predictive energy management of (P)HEVs. Real world road grade data is collected and employed to formulate the ARIMA model, and model predictive control (MPC) is used for the powertrain control. The model is integrated into the predictive energy management strategy to investigate and evaluate the potential gain in fuel economy. Simulation results show that the ARIMA method is able to predict the future road grade with high accuracy, and the corresponding fuel consumption is reduced by at least 4.7%.

Original language	English
Pages (from-to)	2438-2444
Number of pages	7
Journal	Energy Procedia
Volume	105
DOIs	https://doi.org/10.1016/j.egypro.2017.03.700
Publication status	Published - 2017
Event	8th International Conference on Applied Energy, ICAE 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Keywords

ARIMA
HEVs
fuel economy
grade predict
hybrid electric vehicle
predictive energy management

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10.1016/j.egypro.2017.03.700

Cite this

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title = "Road Grade Prediction for Predictive Energy Management in Hybrid Electric Vehicles",

abstract = "The uncertainty caused by the varying of road grades plays a critical role in impacting the hybrid electric vehicles (HEV) energy management performance, and therefore the fuel economy. This paper presents an autoregressive integrated moving average (ARIMA) based method, aiming to forecast the near future road grade in real-time with acceptable accuracy for predictive energy management of (P)HEVs. Real world road grade data is collected and employed to formulate the ARIMA model, and model predictive control (MPC) is used for the powertrain control. The model is integrated into the predictive energy management strategy to investigate and evaluate the potential gain in fuel economy. Simulation results show that the ARIMA method is able to predict the future road grade with high accuracy, and the corresponding fuel consumption is reduced by at least 4.7%.",

keywords = "ARIMA, HEVs, fuel economy, grade predict, hybrid electric vehicle, predictive energy management",

author = "Hongwen He and Jinquan Guo and Chao Sun",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors.; 8th International Conference on Applied Energy, ICAE 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

year = "2017",

doi = "10.1016/j.egypro.2017.03.700",

language = "English",

volume = "105",

pages = "2438--2444",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - Road Grade Prediction for Predictive Energy Management in Hybrid Electric Vehicles

AU - He, Hongwen

AU - Guo, Jinquan

AU - Sun, Chao

PY - 2017

Y1 - 2017

N2 - The uncertainty caused by the varying of road grades plays a critical role in impacting the hybrid electric vehicles (HEV) energy management performance, and therefore the fuel economy. This paper presents an autoregressive integrated moving average (ARIMA) based method, aiming to forecast the near future road grade in real-time with acceptable accuracy for predictive energy management of (P)HEVs. Real world road grade data is collected and employed to formulate the ARIMA model, and model predictive control (MPC) is used for the powertrain control. The model is integrated into the predictive energy management strategy to investigate and evaluate the potential gain in fuel economy. Simulation results show that the ARIMA method is able to predict the future road grade with high accuracy, and the corresponding fuel consumption is reduced by at least 4.7%.

AB - The uncertainty caused by the varying of road grades plays a critical role in impacting the hybrid electric vehicles (HEV) energy management performance, and therefore the fuel economy. This paper presents an autoregressive integrated moving average (ARIMA) based method, aiming to forecast the near future road grade in real-time with acceptable accuracy for predictive energy management of (P)HEVs. Real world road grade data is collected and employed to formulate the ARIMA model, and model predictive control (MPC) is used for the powertrain control. The model is integrated into the predictive energy management strategy to investigate and evaluate the potential gain in fuel economy. Simulation results show that the ARIMA method is able to predict the future road grade with high accuracy, and the corresponding fuel consumption is reduced by at least 4.7%.

KW - ARIMA

KW - HEVs

KW - fuel economy

KW - grade predict

KW - hybrid electric vehicle

KW - predictive energy management

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U2 - 10.1016/j.egypro.2017.03.700

DO - 10.1016/j.egypro.2017.03.700

M3 - Conference article

AN - SCOPUS:85020750650

SN - 1876-6102

VL - 105

SP - 2438

EP - 2444

JO - Energy Procedia

JF - Energy Procedia

T2 - 8th International Conference on Applied Energy, ICAE 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

Road Grade Prediction for Predictive Energy Management in Hybrid Electric Vehicles

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Keywords

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